3) \(\text{Mask RCNN}^{\text{P}}\) . Inspired by  [1]}, we implement a pseudo-labeling based open-set instance segmentation baseline. The idea is to assign pseudo-labels of foreground classes to the background regions (IoU with GT \(<\)  0.5) that have high objectness score. A model is trained to minimize the loss on pseudo-labels.
